In daily life, we tend to present the front of our faces by staring squarely
at a facial recognition machine, instead of facing it sideways, in order to
increase the chance of being correctly recognised. Few-shot-learning (FSL)
classification is challenging in itself because a model has to identify images
that belong to classes previously unseen during training. Therefore, a warped
and non-typical query or support image during testing can make it even more
challenging for a model to predict correctly. In our work, to increase the
chance of correct prediction during testing, we aim to rectify the test input
of a trained FSL model by generating new samples of the tested classes through
image-to-image translation. An FSL model is usually trained on classes with
sufficient samples, and then tested on classes with few-shot samples. Our
proposed method first captures the style or shape of the test image, and then
identifies a suitable trained class sample. It then transfers the style or
shape of the test image to the train-class images for generation of more
test-class samples, before performing classification based on a set of
generated samples instead of just one sample. Our method has potential in
empowering a trained FSL model to score higher during the testing phase without
any extra training nor dataset. According to our experiments, by augmenting the
support set with just 1 additional generated sample, we can achieve around 2%
improvement for trained FSL models on datasets consisting of either animal
faces or traffic signs. By augmenting both the support set and the queries, we
can achieve even more performance improvement. Our Github Repository is
publicly available.

通过图像转换生成样本，并在生成的样本上进行分类，可以提高 few-shot-learning 模型在测试阶段的性能。

FSL 模型得分更高

FSL Model can Score Higher as It Is

Recent advances in Conditional Diffusion Models have led to substantial
capabilities in various domains. However, understanding the impact of
variations in the initial seed vector remains an underexplored area of concern.
Particularly, latent-based diffusion models display inconsistencies in image
generation under standard conditions when initialized with suboptimal initial
seed vectors. To understand the impact of the initial seed vector on generated
samples, we propose a reliability evaluation framework that evaluates the
generated samples of a diffusion model when the initial seed vector is
subjected to various synthetic shifts. Our results indicate that slight
manipulations to the initial seed vector of the state-of-the-art Stable
Diffusion (Rombach et al., 2022) can lead to significant disturbances in the
generated samples, consequently creating images without the effect of
conditioning variables. In contrast, GLIDE (Nichol et al., 2022) stands out in
generating reliable samples even when the initial seed vector is transformed.
Thus, our study sheds light on the importance of the selection and the impact
of the initial seed vector in the latent-based diffusion model.

初期种子向量对生成样本有重要影响，在条件扩散模型中，选择和影响初期种子向量的研究具有重要意义。